Simple guide to AI and ChatGPT

Terms like AI, Machine Learning, Generative AI, and ChatGPT have become part of the language of innovators and professionals. But, how do we learn (and understand) what they are so we can explain it to someone else simply? Perhaps you’ve seen a definition that was hard to understand. What better to simply explain (and demonstrate) what AI, Machine Learning, Generative AI, and ChatGPT are than ChatGPT itself…a transformational Generative AI and Machine Learning technology tool. Here are some explanations that make sense. You can prompt ChatGPT to keep it simple, explain it to a beginner, or use examples and analogies…since they are helpful for understanding. Here are some simple explanations to some key terms changing our future, world, and how we work.

AI Simply Explained

Explain what AI is in just a paragraph or two to someone newer to the concept. Use examples and analogies.

AI, or Artificial Intelligence, is like giving a computer a “brain” that allows it to process information, make decisions, and even learn from experiences, much like humans do. Imagine if you taught a robot to play chess: at first, it might not be very good, but over time, by using AI, it learns from each game and gets better. AI can be seen in everyday tools like Siri or Alexa, which learn to recognize your voice and answer questions, or in recommendation systems on platforms like Netflix that suggest shows based on what you’ve watched before. Just as our brains use past experiences to inform future actions, AI uses vast amounts of data and patterns to make predictions and decisions.

Machine Learning Simply Explained

Now explain machine learning this way.

Machine learning is a subset of AI, much like teaching a computer to become a self-learner. Imagine giving a kid a bunch of colored marbles. Initially, the kid doesn’t know how to sort them, but you guide them a few times by color. Over time, the kid starts recognizing patterns and can sort new marbles without your help. Similarly, in machine learning, instead of programming a computer step-by-step, we feed it data (like the colored marbles) and let it figure out patterns. When you see services like YouTube recommending videos or email filters marking messages as spam, that’s machine learning in action. The system has learned from data to predict what you’d like to watch next or which emails might be unwanted.

Generative AI Simply Explained

Now explain Generative AI this way.

Generative AI is like giving an artist a basic idea and letting them create a unique piece of art from it. Imagine telling a child to draw a “fantasy castle.” Even if the child has never seen one, they might combine elements of different things they’ve seen before, like towers from a regular castle and wings from a fairy tale, to craft something new. In the world of AI, generative models work similarly. Instead of just recognizing patterns or making decisions based on data, they can create entirely new content. When you see AI composing music, designing artwork, or even generating realistic images of people who don’t exist, that’s generative AI at work. It’s taken inspiration from vast amounts of data and used it to create something original.

ChatGPT Simply Explained

Now explain ChatGPT this way.

ChatGPT is like a super-knowledgeable pen pal in a letter-writing game. Imagine you write letters to this pen pal about any topic, from history to recipes, and they always write back with detailed answers or stories. They’ve read a ton of books, seen many movies, and heard countless stories, so their responses are based on all that information. However, there’s a catch: they don’t have feelings, memories, or consciousness; they only know what’s in those books and stories. ChatGPT operates similarly. You type questions or comments, and it responds based on vast amounts of text it’s been trained on. It doesn’t truly “understand” or “feel” but is great at mimicking human conversation based on the patterns in the data it’s seen.

Natural Language Processing (NLP) Simply Explained

Now explain Natural Language Processing this way.

Natural Language Processing (NLP) is like teaching a robot to understand and speak human languages. Imagine if you had a pet parrot that, instead of just mimicking sounds, could actually understand what you said and respond thoughtfully. You might ask it about the weather or to tell you a story, and it’d reply appropriately. NLP is the magic behind making computers understand and generate human language. It’s the reason why tools like Siri can answer when you ask, “What’s the weather today?” or why Google Translate can convert sentences from one language to another. Instead of just hearing sounds or seeing letters, NLP-enabled machines grasp the meaning and context of words, much like our parrot understanding and replying to our chats.

Large Language Model (LLMs) Simply Explained

Now explain Large Language Model (LLMs) this way.

A Large Language Model (LLM) is like a super-charged, ultra-experienced storyteller. Imagine a grandparent who has read every book in the world, listened to countless conversations, and remembered every story ever told to them. If you ask them about a topic, they can weave a detailed tale or provide insightful answers based on all that they’ve absorbed. LLMs, like ChatGPT, operate in a similar way. They’ve been trained on vast amounts of text, so when you pose a question or provide a prompt, they generate responses based on patterns and information from that massive data. It’s like having a conversation with a library that’s come to life, always ready to share knowledge or spin a yarn.

Thanks for these explanations ChatGPT! Now explain how all of these terms come together to make ChatGPT do what it does.

Alright, imagine ChatGPT as a master chef in a state-of-the-art kitchen. AI is the foundation of the kitchen, providing all the tools and equipment. Machine learning is like the training the chef underwent, learning from countless recipes and cooking experiences. Natural Language Processing (NLP) is the skill that lets the chef understand your specific food preferences and dietary needs when you describe them. The Large Language Model (LLM) is the vast cookbook the chef refers to, containing recipes and culinary knowledge from all over the world. When you ask for a dish or describe a flavor, the chef, using the tools in the kitchen, his training, understanding of your request, and the enormous cookbook, prepares a meal tailored for you. In ChatGPT’s case, that “meal” is the detailed, relevant, and conversational response you receive based on your input, all powered by the combined might of AI, machine learning, NLP, and LLMs.

ChatGPT Generative AI Explanation Conclusion

We hope these simple explanations with examples and analogies helped you to understand each of these terms! This was a demonstration of another great use case for ChatGPT…helping us to understand a complex topic. Remember, keep prompting ChatGPT to get the explanation that is understandable for you in a way that you learn and remember. Here are some other articles about ChatGPT you may find useful.